CN107356871A - A kind of motor monitoring method and device - Google Patents
A kind of motor monitoring method and device Download PDFInfo
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- CN107356871A CN107356871A CN201710525691.0A CN201710525691A CN107356871A CN 107356871 A CN107356871 A CN 107356871A CN 201710525691 A CN201710525691 A CN 201710525691A CN 107356871 A CN107356871 A CN 107356871A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0061—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electrical machines
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/005—Testing of electric installations on transport means
- G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
- G01R31/007—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks using microprocessors or computers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/10—Vehicle control parameters
- B60L2240/12—Speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/10—Vehicle control parameters
- B60L2240/14—Acceleration
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/42—Drive Train control parameters related to electric machines
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- Microelectronics & Electronic Packaging (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The disclosure is directed to a kind of motor monitoring method and device.This method includes：Monitor the energy data that the speed data of electric vehicle is inputted or exported with motor；The deep neural network model of corresponding training in advance is selected according to the speed data；Analyze whether the speed data matches with the energy data according to the deep neural network model；When the speed data and the energy data mismatch, determine that the working condition of the electronic transmitter is abnormal.The technical scheme, for the different conditions (output energy state and inputing power state) of electric motor operation, the working condition of motor is monitored using different depth neural network model.So, the monitoring to electric motor operation state is more accurate, and can find the abnormality of motor in time, improves electric vehicle running efficiency and security.
Description
Technical field
This disclosure relates to electric vehicle technical field, more particularly to a kind of motor monitoring method and device.
Background technology
At present, with the development of science and technology and the getting worse of problem of environmental pollution, the development of the electric vehicle of environmental protection and energy saving
It is further important, and have begun to gradually popularize.For example, the High Speed Railway Trains in China are just travelled using electric power as the energy,
Also, electric automobile also abandons car in progressively substitution fuel oil, turns into one of main means of transport of urban transportation.
Therefore, the safe and effective traveling of electric vehicle is as major issue, increasingly by popular of interest, and electric vehicle
The monitoring of motor, more becomes the most important thing.
The content of the invention
The embodiment of the present disclosure provides a kind of motor monitoring method and device.The technical scheme is as follows：
According to the first aspect of the embodiment of the present disclosure, there is provided a kind of motor monitoring method, including：
Monitor the energy data that the speed data of electric vehicle is inputted or exported with motor；
The deep neural network model of corresponding training in advance is selected according to the speed data；
Analyze whether the speed data matches with the energy data according to the deep neural network model；
When the speed data and the energy data mismatch, determine that the working condition of the electronic transmitter is different
Often.
Optionally, the speed data includes the travel speed data and acceleration information of the electric vehicle；The depth
Spending neural network model includes the travel speed data, the corresponding relation of acceleration information and the energy data.
Optionally, the energy data includes the first energy data of motor output, the deep neural network
Model includes the first model, and first model includes the travel speed data, acceleration information and the first electric energy number
According to corresponding relation；
The energy data that the speed data of electric vehicle is inputted or exported with motor is monitored, including：
Monitor the travel speed data and acceleration information of the electric vehicle, and the first electric energy of motor output
Data；
The deep neural network model of corresponding training in advance is selected according to the speed data, including：
When determining that the electric vehicle is at the uniform velocity travelled or given it the gun according to the travel speed data and acceleration information
When, obtain first model；
Analyze whether the speed data matches with the energy data according to the deep neural network model, including：
According to first model, analyzing the travel speed data and acceleration information and first energy data is
No matching.
Optionally, the energy data includes the second energy data of the electronic transmitter input；The depth nerve
Network model includes the second model, and second model includes the travel speed data, acceleration information and the described second electricity
The corresponding relation of energy data；
The energy data that the speed data of electric vehicle is inputted or exported with motor is monitored, including：
Monitor the travel speed data and acceleration information of the electric vehicle, and the second electric energy of motor input
Data；
The deep neural network model of corresponding training in advance is selected according to the speed data, including：
When determining the electric vehicle Reduced Speed Now according to the travel speed data and acceleration information, described in acquisition
Second model；
Analyze whether the speed data matches with the energy data according to the deep neural network model, including：
According to second model, analyzing the travel speed data and acceleration information and second energy data is
No matching.
Optionally, when the speed data and the energy data mismatch, the work of the electronic transmitter is determined
Abnormal state, including：
When determining the speed data energy data exception according to the deep neural network model, really
The fixed motor is abnormal；
When determining the speed data exception and the normal energy data according to the deep neural network model, really
The transmission system of the fixed electronic transmitter connection is abnormal.
According to the second aspect of the embodiment of the present disclosure, there is provided a kind of motor monitoring device, including：
Monitoring modular, for monitoring the speed data and motor input or the energy data exported of electric vehicle；
Selecting module, for selecting the deep neural network model of corresponding training in advance according to the speed data；
Analysis module, it is with the energy data for analyzing the speed data according to the deep neural network model
No matching；
Determining module, for when the speed data and energy data mismatch, determining the electronic transmitter
Working condition it is abnormal.
Optionally, the speed data includes the travel speed data and acceleration information of the electric vehicle；The depth
Spending neural network model includes the travel speed data, the corresponding relation of acceleration information and the energy data.
Optionally, the energy data includes the first energy data of motor output, the deep neural network
Model includes the first model, and first model includes the travel speed data, acceleration information and the first electric energy number
According to corresponding relation；
The monitoring modular, for monitoring the travel speed data and acceleration information of the electric vehicle, and the electricity
First energy data of motivation output；
The selecting module, determine that the electric vehicle is even according to the travel speed data and acceleration information for working as
When speed is travelled or given it the gun, first model is obtained；
The analysis module, for according to first model, analyze the travel speed data and acceleration information with
Whether first energy data matches.
Optionally, the energy data includes the second energy data of the electronic transmitter input；The depth nerve
Network model includes the second model, and second model includes the travel speed data, acceleration information and the described second electricity
The corresponding relation of energy data；
The monitoring modular, for monitoring the travel speed data and acceleration information of the electric vehicle, and the electricity
Second energy data of motivation input；
The selecting module, determine that the electric vehicle subtracts according to the travel speed data and acceleration information for working as
During speed traveling, second model is obtained；
The analysis module, for according to second model, analyze the travel speed data and acceleration information with
Whether second energy data matches.
Optionally, the determining module, the speed data is being determined just according to the deep neural network model for working as
During the energy data exception, determine that the motor is abnormal；Described in being determined according to the deep neural network model
When the extremely described energy data of speed data is normal, determine that the transmission system of the electronic transmitter connection is abnormal.
The technical scheme provided by this disclosed embodiment can include the following benefits：
In the present embodiment, according to deep neural network model corresponding to the selection of the speed data of electric vehicle, i.e., for electricity
The different conditions (output energy state and inputing power state) of motivation work, just with different depth neural network model to electronic
The working condition of machine is monitored.So, the monitoring to electric motor operation state is more accurate, and can find in time electronic
The abnormality of machine, improve electric vehicle running efficiency and security.
It should be appreciated that the general description and following detailed description of the above are only exemplary and explanatory, not
The disclosure can be limited.
Brief description of the drawings
Accompanying drawing herein is merged in specification and forms the part of this specification, shows the implementation for meeting the disclosure
Example, and be used to together with specification to explain the principle of the disclosure.
Fig. 1 is a kind of flow chart of motor monitoring method according to an exemplary embodiment.
Fig. 2 is a kind of flow chart of motor monitoring method according to another exemplary embodiment.
Fig. 3 is a kind of flow chart of motor monitoring method according to another exemplary embodiment.
Fig. 4 is a kind of block diagram of motor monitoring device according to an exemplary embodiment.
Embodiment
Here exemplary embodiment will be illustrated in detail, its example is illustrated in the accompanying drawings.Following description is related to
During accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawings represent same or analogous key element.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistent with the disclosure.On the contrary, they be only with it is such as appended
The example of the consistent apparatus and method of some aspects be described in detail in claims, the disclosure.
The motor monitoring of the disclosure, can be carried out in electric vehicle, or, also can remotely it monitor, i.e., by wireless
Network inputs the speed data of electric vehicle and motor or the energy data of output is sent to Surveillance center and carries out motor
Monitoring.
The work of the motor of the electric vehicles such as high ferro train, electric automobile includes two different stages.At the uniform velocity going
When sailing or giving it the gun, motor output electric energy is converted into mechanical energy so that electric vehicle can be travelled with certain speed, output electricity
It can increase, then car speed improves.When electric vehicle Reduced Speed Now, the mechanical energy of electric vehicle is converted into electric energy and is input to electricity
Motivation.
The technical scheme of the disclosure, using two models, respectively electric vehicle is when at the uniform velocity travelling or giving it the gun pair
Corresponding second model when the first model answered and electric vehicle Reduced Speed Now, according to the two models, split-phase motor work
Whether state is normal.In two models, not only including electric motor normal working when speed data corresponding to energy data, also may be used
Speed data corresponding to energy data during including electric motor state exception.
Therefore, for each electric vehicle, substantial amounts of speed data, and the electric energy number of its motor are gathered in advance
According to by deep neural network Algorithm Learning and training, obtaining two models.
Fig. 1 is a kind of flow chart of motor monitoring method according to an exemplary embodiment, as shown in figure 1, should
Method can be applicable to electric vehicle end, can also be applied to remote monitoring center, and this method comprises the following steps：
Step S11, monitor the speed data and motor input or the energy data exported of electric vehicle；
Step S12, the deep neural network model of corresponding training in advance is selected according to speed data；
Step S13, whether matched with energy data according to deep neural network model analyze speed data；
Step S14, when speed data and energy data mismatch, determine that the working condition of electronic transmitter is abnormal.
In the present embodiment, according to deep neural network model corresponding to the selection of the speed data of electric vehicle, i.e., for electricity
The different conditions (output energy state and inputing power state) of motivation work, using different depth neural network model to electronic
The working condition of machine is monitored.So, the monitoring to electric motor operation state is more accurate, and can find in time electronic
The abnormality of machine, improve electric vehicle running efficiency and security.
Wherein, speed data includes the travel speed data and acceleration information of electric vehicle；Deep neural network model
Include the corresponding relation of travel speed data, acceleration information and energy data.
Fig. 2 is a kind of flow chart of motor monitoring method according to another exemplary embodiment, as shown in Fig. 2
In one embodiment, energy data includes the first energy data of motor output, and deep neural network model includes first
Model, the first model include the corresponding relation of travel speed data, acceleration information and the first energy data, i.e. the first model is
Electric vehicle at the uniform velocity or gives it the gun, corresponding deep neural network model during motor output electric energy.
Step S21, monitor the travel speed data and acceleration information of electric vehicle, and the first electric energy of motor output
Data；
Step S22, when determining that electric vehicle is at the uniform velocity travelled or given it the gun according to travel speed data and acceleration information
When, obtain the first model；
Step S23, according to the first model, analyze travel speed data and acceleration information and the first energy data whether
Match somebody with somebody.
In the present embodiment, determine that electric vehicle is at the uniform velocity travelled or given it the gun according to travel speed data and acceleration information
When, i.e., motor is in the working condition of output electric energy, and the first model corresponding to selection carries out Data Matching, judges motor work
Whether abnormal make state.So, the monitoring to electric motor operation state is more accurate, and can find the different of motor in time
Normal state, improve electric vehicle running efficiency and security.
Fig. 3 is a kind of flow chart of motor monitoring method according to another exemplary embodiment, as shown in figure 3,
In one embodiment, energy data includes the second energy data of electronic transmitter input；Deep neural network model includes
Second model, the second model include the corresponding relation of travel speed data, acceleration information and the second energy data, i.e. the second mould
Type is electric vehicle Reduced Speed Now, corresponding deep neural network model during motor inputing power.
Step S31, monitor the travel speed data and acceleration information of electric vehicle, and the second electric energy of motor input
Data；
Step S32, when determining electric vehicle Reduced Speed Now according to travel speed data and acceleration information, obtain second
Model；
Step S33, according to the second model, analyze travel speed data and acceleration information and the second energy data whether
Match somebody with somebody.
It is when determining electric vehicle Reduced Speed Now according to travel speed data and acceleration information, i.e., electronic in the present embodiment
Machine is in the working condition of inputing power, and the second model corresponding to selection carries out Data Matching, judges that electric motor operation state is
No exception.So, the monitoring to electric motor operation state is more accurate, and can find the abnormality of motor in time,
Improve electric vehicle running efficiency and security.
Above-mentioned steps S13 includes：When determining that speed data is normal and energy data is abnormal according to deep neural network model
When, determine motor exception；When determining speed data exception and normal energy data according to deep neural network model, it is determined that
The transmission system of electronic transmitter connection is abnormal.
For example, when electric vehicle is at the uniform velocity travelled or given it the gun, it is electric according to corresponding to the first model determines present speed
Energy data exception, when being more than the expection energy data in the first model such as actual energy data, that is, reaches same speed, the electricity
Motivation consumes more electric energy, it is likely that the motor is abnormal.
In another example when electric vehicle is at the uniform velocity travelled or given it the gun, current energy data pair is determined according to the first model
The speed data answered is abnormal, as motor consumes certain electric energy, is really not reaching in the first model expected corresponding to the electric energy
Speed, it is likely that the transmission system being connected with the motor is abnormal.
In another example when electric vehicle Reduced Speed Now, i.e. on-position, present speed change pair is determined according to the second model
The energy data answered is abnormal, as the actual electric energy obtained of same speed change motor is less than expected acquisition in the second model
During electric energy, it is likely that the motor is abnormal.
In the present embodiment, the working condition of motor is monitored by different depth neural network model so that right
The monitoring of electric motor operation state is more accurate, and can find the abnormality of motor in time, improves electric vehicle row
Sail efficiency and security.
Following is embodiment of the present disclosure, can be used for performing embodiments of the present disclosure.
Fig. 4 is a kind of block diagram of motor monitoring device according to an exemplary embodiment, and the device can pass through
Software, hardware or both are implemented in combination with as some or all of of electronic equipment.As shown in figure 4, the motor monitors
Device includes：
Monitoring modular 41, for monitoring the speed data and motor input or the energy data exported of electric vehicle；
Selecting module 42, for selecting the deep neural network model of corresponding training in advance according to speed data；
Analysis module 43, for whether being matched with energy data according to deep neural network model analyze speed data；
Determining module 44, for when speed data and energy data mismatch, determining the working condition of electronic transmitter
It is abnormal.
Optionally, speed data includes the travel speed data and acceleration information of electric vehicle；Deep neural network mould
Type includes the corresponding relation of travel speed data, acceleration information and energy data.
Optionally, energy data includes the first energy data of motor output, and deep neural network model includes first
Model, the first model include the corresponding relation of travel speed data, acceleration information and the first energy data；
Monitoring modular 41, for monitoring the travel speed data and acceleration information of electric vehicle, and motor output
First energy data；
Selecting module 42, for when according to travel speed data and acceleration information determine electric vehicle at the uniform velocity travel or add
During speed traveling, the first model is obtained；
Analysis module 43, for according to the first model, analyzing travel speed data and acceleration information and the first electric energy number
According to whether matching.
Optionally, energy data includes the second energy data of electronic transmitter input；Deep neural network model includes
Second model, the second model include the corresponding relation of travel speed data, acceleration information and the second energy data；
Monitoring modular 41, for monitoring the travel speed data and acceleration information of electric vehicle, and motor input
Second energy data；
Selecting module 42, for when determining electric vehicle Reduced Speed Now according to travel speed data and acceleration information,
Obtain the second model；
Analysis module 43, for according to the second model, analyzing travel speed data and acceleration information and the second electric energy number
According to whether matching.
Optionally, determining module 44, speed data normally electric energy number is determined according to deep neural network model for working as
According to it is abnormal when, determine motor exception；When determining that speed data is abnormal and energy data is normal according to deep neural network model
When, determine that the transmission system of electronic transmitter connection is abnormal.
In the present embodiment, according to deep neural network model corresponding to the selection of the speed data of electric vehicle, i.e., for electricity
The different conditions (output energy state and inputing power state) of motivation work, using different depth neural network model to electronic
The working condition of machine is monitored.So, the monitoring to electric motor operation state is more accurate, and can find in time electronic
The abnormality of machine, improve electric vehicle running efficiency and security.
Those skilled in the art will readily occur to the disclosure its after considering specification and putting into practice disclosure disclosed herein
Its embodiment.The application is intended to any modification, purposes or the adaptations of the disclosure, these modifications, purposes or
Person's adaptations follow the general principle of the disclosure and including the undocumented common knowledges in the art of the disclosure
Or conventional techniques.Description and embodiments are considered only as exemplary, and the true scope of the disclosure and spirit are by following
Claim is pointed out.
It should be appreciated that the precision architecture that the disclosure is not limited to be described above and is shown in the drawings, and
And various modifications and changes can be being carried out without departing from the scope.The scope of the present disclosure is only limited by appended claim.
Claims (10)
- A kind of 1. motor monitoring method, it is characterised in that including：Monitor the energy data that the speed data of electric vehicle is inputted or exported with motor；The deep neural network model of corresponding training in advance is selected according to the speed data；Analyze whether the speed data matches with the energy data according to the deep neural network model；When the speed data and the energy data mismatch, determine that the working condition of the electronic transmitter is abnormal.
- 2. according to the method for claim 1, it is characterised in that the speed data includes the traveling speed of the electric vehicle Degrees of data and acceleration information；The deep neural network model include the travel speed data, acceleration information with it is described The corresponding relation of energy data.
- 3. according to the method for claim 2, it is characterised in that the energy data includes the first of motor output Energy data, the deep neural network model include the first model, and first model includes the travel speed data, added Speed data and the corresponding relation of first energy data；The energy data that the speed data of electric vehicle is inputted or exported with motor is monitored, including：Monitor the travel speed data and acceleration information of the electric vehicle, and the first electric energy number of motor output According to；The deep neural network model of corresponding training in advance is selected according to the speed data, including：When determining that the electric vehicle is at the uniform velocity travelled or given it the gun according to the travel speed data and acceleration information, obtain Take first model；Analyze whether the speed data matches with the energy data according to the deep neural network model, including：According to first model, analyze the travel speed data and acceleration information and first energy data whether Match somebody with somebody.
- 4. according to the method in claim 2 or 3, it is characterised in that it is defeated that the energy data includes the electronic transmitter The second energy data entered；The deep neural network model includes the second model, and second model includes the traveling speed The corresponding relation of degrees of data, acceleration information and second energy data；The energy data that the speed data of electric vehicle is inputted or exported with motor is monitored, including：Monitor the travel speed data and acceleration information of the electric vehicle, and the second electric energy number of motor input According to；The deep neural network model of corresponding training in advance is selected according to the speed data, including：When determining the electric vehicle Reduced Speed Now according to the travel speed data and acceleration information, described second is obtained Model；Analyze whether the speed data matches with the energy data according to the deep neural network model, including：According to second model, analyze the travel speed data and acceleration information and second energy data whether Match somebody with somebody.
- 5. according to the method for claim 1, it is characterised in that when the speed data and the energy data mismatch When, determine that the working condition of the electronic transmitter is abnormal, including：When determining the speed data energy data exception according to the deep neural network model, institute is determined State motor exception；When determining the speed data exception and the normal energy data according to the deep neural network model, institute is determined The transmission system for stating electronic transmitter connection is abnormal.
- A kind of 6. motor monitoring device, it is characterised in that including：Monitoring modular, for monitoring the speed data and motor input or the energy data exported of electric vehicle；Selecting module, for selecting the deep neural network model of corresponding training in advance according to the speed data；Analysis module, for according to the deep neural network model analyze the speed data and the energy data whether Match somebody with somebody；Determining module, for when the speed data and energy data mismatch, determining the work of the electronic transmitter Make abnormal state.
- 7. device according to claim 6, it is characterised in that the speed data includes the traveling speed of the electric vehicle Degrees of data and acceleration information；The deep neural network model include the travel speed data, acceleration information with it is described The corresponding relation of energy data.
- 8. device according to claim 7, it is characterised in that the energy data includes the first of motor output Energy data, the deep neural network model include the first model, and first model includes the travel speed data, added Speed data and the corresponding relation of first energy data；The monitoring modular, for monitoring the travel speed data and acceleration information of the electric vehicle, and the motor First energy data of output；The selecting module, determine that the electric vehicle is at the uniform velocity gone according to the travel speed data and acceleration information for working as When sailing or giving it the gun, first model is obtained；The analysis module, for according to first model, analyze the travel speed data and acceleration information with it is described Whether the first energy data matches.
- 9. the device according to claim 7 or 8, it is characterised in that it is defeated that the energy data includes the electronic transmitter The second energy data entered；The deep neural network model includes the second model, and second model includes the traveling speed The corresponding relation of degrees of data, acceleration information and second energy data；The monitoring modular, for monitoring the travel speed data and acceleration information of the electric vehicle, and the motor Second energy data of input；The selecting module, the electric vehicle deceleration row is determined according to the travel speed data and acceleration information for working as When sailing, second model is obtained；The analysis module, for according to second model, analyze the travel speed data and acceleration information with it is described Whether the second energy data matches.
- 10. device according to claim 6, it is characterised in that the determining module, for when according to depth nerve When network model determines the speed data energy data exception, determine that the motor is abnormal；When according to institute State that deep neural network model determines that the speed data is abnormal when the energy data is normal, determine the electronic transmitter The transmission system of connection is abnormal.
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| CN109900459A (en) * | 2017-12-08 | 2019-06-18 | 嘉兴博感科技有限公司 | A kind of state monitoring method and system of rail traffic hook buffer |
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